Automated Classification of Severity in Cardiac Dyssynchrony Merging Clinical Data and Mechanical Descriptors
المؤلفون المشاركون
Vallejo, Enrique
Santos-Díaz, Alejandro
Hernández, Salvador
Jiménez-Ángeles, Luis
Valdés-Cristerna, Raquel
المصدر
Computational and Mathematical Methods in Medicine
العدد
المجلد 2017، العدد 2017 (31 ديسمبر/كانون الأول 2017)، ص ص. 1-9، 9ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2017-02-19
دولة النشر
مصر
عدد الصفحات
9
التخصصات الرئيسية
الملخص EN
Cardiac resynchronization therapy (CRT) improves functional classification among patients with left ventricle malfunction and ventricular electric conduction disorders.
However, a high percentage of subjects under CRT (20%–30%) do not show any improvement.
Nonetheless the presence of mechanical contraction dyssynchrony in ventricles has been proposed as an indicator of CRT response.
This work proposes an automated classification model of severity in ventricular contraction dyssynchrony.
The model includes clinical data such as left ventricular ejection fraction (LVEF), QRS and P-R intervals, and the 3 most significant factors extracted from the factor analysis of dynamic structures applied to a set of equilibrium radionuclide angiography images representing the mechanical behavior of cardiac contraction.
A control group of 33 normal volunteers (28±5 years, LVEF of 59.7%±5.8%) and a HF group of 42 subjects (53.12±15.05 years, LVEF < 35%) were studied.
The proposed classifiers had hit rates of 90%, 50%, and 80% to distinguish between absent, mild, and moderate-severe interventricular dyssynchrony, respectively.
For intraventricular dyssynchrony, hit rates of 100%, 50%, and 90% were observed distinguishing between absent, mild, and moderate-severe, respectively.
These results seem promising in using this automated method for clinical follow-up of patients undergoing CRT.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Santos-Díaz, Alejandro& Valdés-Cristerna, Raquel& Vallejo, Enrique& Hernández, Salvador& Jiménez-Ángeles, Luis. 2017. Automated Classification of Severity in Cardiac Dyssynchrony Merging Clinical Data and Mechanical Descriptors. Computational and Mathematical Methods in Medicine،Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1142056
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Santos-Díaz, Alejandro…[et al.]. Automated Classification of Severity in Cardiac Dyssynchrony Merging Clinical Data and Mechanical Descriptors. Computational and Mathematical Methods in Medicine No. 2017 (2017), pp.1-9.
https://search.emarefa.net/detail/BIM-1142056
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Santos-Díaz, Alejandro& Valdés-Cristerna, Raquel& Vallejo, Enrique& Hernández, Salvador& Jiménez-Ángeles, Luis. Automated Classification of Severity in Cardiac Dyssynchrony Merging Clinical Data and Mechanical Descriptors. Computational and Mathematical Methods in Medicine. 2017. Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1142056
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1142056
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر